Abstract
Research on language acquisition for academic purposes is not extensive. In this work, we propose to build a system for recognizing teaching activities from automatic transcriptions of classroom audio and video recordings centered on the professor’s discourse. To this end, we identified the main teaching activities that cover the nature of the lecturer discourse when giving a course e.g. ‘theoretical explanation’, real-world practical example’, interaction lecturer-student’, ‘course-related asides’, etc. We labeled a dataset of lecture transcriptions from a repository with an approximate length of 50 h and we build a classifier by fine-tuning the XLM-RoBERTa model with a classification head on top of it. The results will show that our proposal is a promising step ahead towards recognition of discourse activities in academic contexts.
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Notes
- 1.
MLLP transcriptions. https://ttp.mllp.upv.es/index.php?page=faq.
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Diosdado, D., Romero, A., Onaindia, E. (2021). Recognition of Teaching Activities from University Lecture Transcriptions. In: Alba, E., et al. Advances in Artificial Intelligence. CAEPIA 2021. Lecture Notes in Computer Science(), vol 12882. Springer, Cham. https://doi.org/10.1007/978-3-030-85713-4_22
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DOI: https://doi.org/10.1007/978-3-030-85713-4_22
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